Agentic AI for Enterprise: Emerging Applications and Real-world Challenges

Large language models (LLMs) have revolutionized natural language processing, enabling unprecedented capabilities in reasoning, planning, and tool utilization. Enterprises are increasingly adopting LLM-powered agents to automate complex workflows, from meeting summarization (e.g., Microsoft Copilot) to supply chain optimization and customer service orchestration. However, deploying agentic AI systems in enterprise settings introduces unique challenges, including decision making under uncertainty, multi-agent collaboration, security vulnerabilities, and trust gaps in mission-critical applications. This workshop aims to bridge the gap between academia and industry to explore LLM-driven agentic systems tailored for enterprise needs. We focus on three pillars: 1) emerging architectures that enable dynamic task decomposition and tool invocation; 2) domain-specific applications such as case studies in supply chain and employee productivity domain; 3) evaluation and governance such as the AAEF (Agentic Application Evaluation Framework) and security strategies.